A Fine Slice Of SQL Server
In this video, I take a look at an alternative to bootstrap aggregation & random forest: boosting. We cover a brief history of boosting and see how it works in action with XGBoost and LightGBM.
This is probably the video with the single largest number of links in my show notes. It’s also one of the shortest in the series; it’s funny how things work out sometimes.
Comments closedJohn Cook talks about my favorite form of regression that serves to solve classification problems:
Logistic regression models the probability of a yes/no event occurring. It gives you more information than a model that simply tries to classify yeses and nos. I advised a client to move from an uninterpretable classification method to logistic regression and they were so excited about the result that they filed a patent on it.
It’s too late to patent logistic regression, but they filed a patent on the application of logistic regression to their domain. I don’t know whether the patent was ever granted.
Read on for a few more thoughts on and around logistic regression and logits from a mathematician.
Comments closedIn this video, I cover a powerful ensemble method for classification: random forests. We get an idea of how this differs from CART, learn the best possible metaphor for random forests, and dig into random search for hyperparameter optimization.
Click through to see the video in all its glory.
Comments closedSteven Sanderson talks about the Akaike Information Criterion:
In the world of data analysis and statistics, one of the key challenges is selecting the best model to describe and analyze your data. This decision is crucial because it impacts the accuracy and reliability of your results. Among the many tools available, the Akaike Information Criterion (AIC) stands out as a powerful method for comparing different models and choosing the most suitable one.
Today we will go through an example of model selection using the AIC, specifically focusing on its application to various statistical distributions available in the TidyDensity package. TidyDensity, a part of the healthyverse ecosystem, offers a comprehensive suite of tools for data analysis in R, including functions to compute AIC scores for different probability distributions.
Read on for a quick primer on the AIC itself and how you can use it in TidyDensity.
Comments closedSteven Sanderson performs some sampling:
In the area of statistical modeling and Bayesian inference, Markov Chain Monte Carlo (MCMC) methods are indispensable tools for tackling complex problems. The new
tidy_mcmc_sampling()function in the TidyDensity R package simplifies MCMC sampling and visualization, making it accessible to a broader audience of data enthusiasts and analysts.
Read on for a brief primer on MCMC and an example of how the tidy_mcmc_sampling() function works.
Steven Sanderson achieves normality:
In data analysis, especially when dealing with multiple samples or distributions, ensuring comparability and removing biases is crucial. One powerful technique for achieving this is quantile normalization. This method aligns the distributions of values across different samples, making them more similar in terms of their statistical properties.
Read on to see how you can use the TidyDensity package to pull this off.
Comments closedIn this video, I explain some core concepts behind classification and introduce the first classification algorithm we will look at in CART.
CART, by the way, stands for Classification and Regression Trees, and is one of the easiest classification algorithms to understand as a concept: it’s a decision tree (aka, a series of if-else statements) where each terminal node is an outcome: either a class for classification or a value for regression.
Comments closedKeith Goldfield updates the simstudy package:
A researcher reached out to me a few weeks ago. They were trying to generate longitudinal data that included irregularly spaced follow-up periods. The default periods generated by the function
addPeriodsin thesimstudypackage are {0,1,2,…,n−1}{0,1,2,…,n−1}, where there are n total periods. However, when follow-up periods required more specificity, such as {0,90,180,365}{0,90,180,365} days from baseline, users had to manually add them. Originally, I had intended to incorporate this feature into the function, but unfortunately it slipped through the cracks. Thanks to the clear motivation provided by the researcher, I’ve implemented this enhancement. Users can now replace the default vector with their desired set of follow-up periods using the new argument periodVec. This addition is available in the development version ofsimstudyon GitHub.
Read on to see how it works. H/T R-Bloggers.
Comments closedSteven Sanderson performs a test:
In the world of statistics and data analysis, understanding and accurately estimating the parameters of probability distributions is crucial. One such distribution is the chi-square distribution, often encountered in various statistical analyses. In this blog post, we’ll dive into how we can estimate the degrees of freedom (“df”) and the non-centrality parameter (“ncp”) of a chi-square distribution using R programming language.
Read on to learn more about the process of estimation while I grumble something about Bayesian analysis being better.
Comments closedSteven Sanderson is from the 5th dimension:
Visualizing similarities between data points can be tricky, especially when dealing with many features. This is where multidimensional scaling (MDS) comes in handy. It allows us to explore these relationships in a lower-dimensional space, typically 2D or 3D for easier interpretation. In R, the
cmdscale()function from base R and is a great tool for performing classical MDS.
Click through to see how this works. In case you’re curious, cmdscale() is an example of principal coordinates analysis. If you’re familiar with principal components analysis, that’s a different form of multidimensional scaling.